Probability Sampling

Probability sampling is an approach where a sample from a larger population is selected using a method based on probability. For example, for a participant to be considered as a probability sample, he/she must be selected using a random selection. In probability sampling, everyone in a population should have an equal chance of selection. For example, if there are 1000 students in the school(population) than all should have the same probability to be selected. Probability sampling gives a good result to create a sample in a population. It uses statistical theory to select a sample randomly, a small number of variables from a large population and then predicts whether all their responses match each other or not.

Types of Probability Sampling

1. Simple random sampling — In this, we take a completely random method of selecting samples. This is a very easy method to select samples. These processes consist of assigning numbers to all variables and then using a random number generator to select samples. There are two ways of sampling in simple random sampling i.e. Lottery system and random number generator. It works with a small population. For example, there are 100 students in a class, 10 students are randomly selected by the teacher to a certain task.

Advantages of simple random sampling

1. Sample is easy to select

2. Lack of bias

3. Requires less experience and knowledge

4. It provides piece a information with a lower chance of data errors

Disadvantages of simple random sampling

l. Time consuming

2. Difficult to access a list of the full population

3. High cost

2. Stratified random sampling — This process includes splitting variables into groups and then selecting samples randomly using assigned groups. Dividing variable into commonly exclusive groups and then using simple random sampling to select a variable from groups for samples. For example — there are 12 balls in a bag with different colours. So, we divided balls into groups with there colours i.e. red, blue, and green and then we select balls randomly from those groups.

Advantages of stratified random sampling

l. Can acquire information about the whole information

2. Less variability

Disadvantages of stratified random sampling

l. Can’t be used in all studies

2. Sampling error is difficult to measure

3. Systematic sampling — In this process, we select the “nth” term from the population as samples. Systematic sampling is an extended implementation of the same old probability technique in which each member of the group is selected at regular periods to form a sample. For example, we have ten people and we have selected every 2nd person from the population.

Advantages of systematic sampling

l. Simple technique

2. Sample is very accurate to population

3. Sampling error easily measured

Disadvantages of systematic sampling

l. Need a complete list of variables

2. Sample can be biased

4. Cluster random sampling — In this process, we randomly select variables from a population that is too large for simple random sampling. Cluster sampling commonly analyzes a particular population in which the sample consists of more than a few variables, for example, city, family, university etc. The clusters are then selected by dividing the greater population into various smaller sections. For example, selecting 2000 peoples from the entire population of India.

Advantages of cluster random sampling

l. It grants for research to be operated with a reduced economy.

2. It reduces variability

3. It is more suitable

Disadvantages of cluster random sampling

l. More sampling errors

2. Clusters may have the same data points

3. Requires a minimum number of cases for accuracy

5. Multi-stage random sampling — It uses a combination of all sampling methods. In this method, the selected variable from population and then they divided into significant group and then divided into sub-groups to make it simpler for primary data collection.

Advantages of multi-stage random sampling

l. It is time and cost-effective because it cuts down the population in smaller groups.

2. Researcher can make groups and sub-groups until they get the desired size and type of group.

3. It is very useful to create samples from geographical dispersed population.

Disadvantages of multi-stage random sampling

1. It is not accurate as simple random sampling.

2. More testing is difficult to do.

3. Require plenty of data.

Advantages of probability distribution

l. Systematic error and sampling bias is absent

2. Reliability of research finding is high

3. Increased accuracy of sampling error estimation

4. The possibility to make inferences about the population

5. Samples are highly representative to the population

6. Easiest method for sampling

7. It doesn’t need any technical knowledge

Disadvantages of probability distribution

l. Make no use of auxiliary information

2. Not be a representative of the whole population

3. More time consuming

4. Highly complex-ed when compared to non-probability sampling